1. Predicting Depression Occurrence Using Classification Algorithm in Data Mining
Abdur Rahman
Department of Statistics
Shahjalal University of Science and Technology
Sylhet, Bangladesh

2. Introduction Universal definition of old age is elusive
Only 6.13 percent is elder (60+) in Bangladesh
Become senile and lose ability in physically and mentally
Aging is one of the embryonic problems in Bangladesh
Self-assessments of health are common components of population- based surveys
Elderly are found to suffer from diseases like depression, sleeping problem, gastric problem, diabetes, mental problem and so on

3. Methodology Linear Discriminant Analysis (LDA)
Quadratic Discriminant Analysis (QDA)
Logistic Regression Analysis
K-Nearest Neighbor (KNN)

4. Figure: Architecture of Classification Algorithm

5. Sampling Method Cluster sampling
Urban area, rural area, tea garden area and ethnic area
Collected whole population from each cluster

6. Data Primary data Collected during March to September 2015
229 elderly peoples aged ranges from 60 to 60+
Face to face personal interviews through questionnaires

7. Linear Discriminant Analysis
LDA undertakes the same task as Logistic Regression. It classifies data based on categorical variables
Making profit or not
Buy a product or not
Satisfied customer or not
Political party voting intention

8. Linear Discriminant Analysis
LDA involves the determination of linear equation (just like linear regression) that will predict which group the case belongs to.
Here
D: discriminant function
v: discriminant coefficient or weight for the variable
X: variable
a: constant

9. Quadratic Discriminant Analysis
Quadratic discriminant analysis calculates a Quadratic Score Function
This is a function of population mean vectors and the variance- covariance matrices for the ith group

10. Logistic Regression
In logistic regression, the dependent variable is binary or dichotomous, i.e. it only contains data coded as 1 (TRUE, success, pregnant, etc.) or 0 (FALSE, failure, non- pregnant, etc.)
The logit transformation is defined as the logged odds

11. KNN
KNN is completely non-parametric: No assumptions are made about the shape of the decision boundary!
We can expect KNN to dominate both LDA and Logistic Regression when the decision boundary is highly non-linear
The most intuitive nearest neighbour type classifier is the one nearest neighbour classifier that assigns a point x to the class of its closest neighbour in the feature space, that is

12. Figure: Error Rate for Different Value of K

13. Results & Discussions If the true decision boundary is
Linear: LDA and Logistic outperforms
Moderately Non-linear: QDA outperforms
More complicated: KNN is superior

14. Correctly Classified (%)
Results & Discussions
Correctly Classified (%)
Misclassified (%)
QDA
93.67
6.33
LDA
94.94
5.06
Logistic Regression
KNN
98.73
1.27

15. Figure: Graphical Representation of Accuracy

16. Conclusions LDA and Logistic regression shows same accuracy
QDA performs lowest accuracy
KNN is better than LDA, QDA and Logistic regression

17. THANK YOU